Documents publiés en "2026"

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Dubé, J., Ciari, F., Gharavi, S., Hassan, R., & Naseri, H. (2026). Les impacts du télétravail sur la demande en transport : Évaluation basée sur un modèle de simulation basée sur un modèle de simulation par agents appliqués à Québec. (Rapport technique n° 2026RP-15). Lien externe

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Ghavami, S., & Naseri, H. (2026). Advanced Machine Learning Techniques for Predicting Compaction and Strength Characteristics of Cement Kiln Dust-Stabilized Soils. Transportation Infrastructure Geotechnology, 13(4), 84 (23 pages). Lien externe

Ghavami, S., Naseri, H., & Safi Jahanshahi, F. (2026). Advanced Machine Learning Techniques for Predicting the California Bearing Ratio of Soils Stabilized with Cement Kiln Dust. International Journal of Pavement Research and Technology, 15 pages. Lien externe

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Hassan, R., Naseri, H., Gharavi, S., Dubé, J., & Ciari, F. (2026). Understanding Teleworking Propensity Using Interpretable Machine Learning: Evidence from Québec City. Data Science for Transportation, 8(2), 20 (17 pages). Lien externe

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Javadian, M. A., Wang, B., Waygood, O., Naseri, H., & Ji, X. (juin 2026). What drives intention to shift from car to the Québec tramway? A machine learning analysis [Présentation]. Dans CSCE Annual Conference 2026, Québec, Québec, Canada. Non disponible

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Mohammadtaheri, H., Sedaghati, R., Molavi-Zarandi, M., Naseri, H., Bonakdar, A., & Vautrin, Y. (2026). An advanced finite element–AI coupled framework for high–fidelity and efficient prediction of residual stresses and distortions in laser powder bed fusion. Computational Mechanics, 21 pages. Lien externe

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Naseri, H., Ciari, F., & Saunier, N. (2026). A New Hybrid Model for Optimal Bike-Sharing Locations in Montreal. Data Science for Transportation, 8(2), 18 (21 pages). Lien externe

Naseri, H., Ciari, F., & Saunier, N. (2026). Towards Sustainable Urban Mobility: Interpretable Machine Learning for Bike-Sharing Inflow and Outflow Prediction. Data Science for Transportation, 8, 27 (20 pages). Lien externe

Naseri, H., Laviolette, J., Waygood, O., & Manaugh, K. (2026). Unveiling barriers and motivations to cycling across different stages of change. Transportation Research Part F Traffic Psychology and Behaviour, 119, 103572 (20 pages). Lien externe

Naseri, H., Waygood, O., Wang, B., & Laviolette, J. (2026). Transportation and climate behaviors: Comparing difficulty of transportation-related behaviors across different populations. Journal of Environmental Psychology, 114, 103173 (11 pages). Lien externe

Naseri, H., Waygood, O., Wang, B., Daziano, R. A., & Feinberg, M. (2026). Role of GHG information presentation in shaping EV preferences: machine learning approach. Transportation Research Part D Transport and Environment, 155, 105329 (20 pages). Lien externe

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Patwary, A. U. Z., Ciari, F., Angioloni, L., Naseri, H., Brusci, L., & Iannelli, G. (2026). Bridging AI and Traffic Simulation: A Robust and Comprehensive Framework for LLM-Based AI Replanning Agents in MATSim. [Présentation]. Procedia Computer Science, 280, 622-629. Présentée à 17th International Conference on Ambient Systems, Networks and Technologies Networks (ANT 2026), Istanbul, Türkiye. Lien externe

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Safi Jahanshahi, F., Reza Ghanizadeh, A., Naseri, H., & Mouldi, A. (2026). Interpretable AI Models Based on Hybrid Ensemble Learning Methods for Predicting Unconfined Compressive Strength of Cement-Stabilized Magnetite Iron Ore Tailing. AI, 7(2), 37 (27 pages). Lien externe

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Waygood, O., Naseri, H., Laviolette, J., & Wang, B. (2026). Analyzing the opinions of Canadians on the impacts of electric vehicles. International Journal of Sustainable Transportation, 21 pages. Accès restreintDisponible